The document discusses the implementation of a precision medicine platform at Seoul National University Hospital (SNUH), highlighting its integration with Syapse to enhance cancer treatment through genomic profiling and data analysis. It covers the evolution of precision medicine, specific training and workflow collaborations with Syapse, and the legal compliance regarding data usage in healthcare cloud services. Emphasizing SNUH's commitment to high-quality education, research, and patient care, it explores advancements in cancer diagnostics and treatment methodologies.

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2019 의료IT의 미래 :
서울대학교병원 정밀의학 데이터 플랫폼 도입 사례
Precision Medicine Platform in SNUH
Kyung Hwan Kim
MD, PhD. Professor, CIO
Office for Hospital Information System
Department of Thoracic & Cardiovascular Surgery
Seoul National University Hospital, Seoul, Korea

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Professor
Department of Thoracic & Cardiovascular Surgery
Chief Information Officer
Office of Hospital Information
Seoul National University Hospital, Seoul, Korea
Speaker

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No real or apparent conflicts of interest to report.
Conflict of Interest

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 서울대학교병원 소개
 정밀의료란 무엇인가?
 서울대학교병원의 정밀의료
 서울대학교병원과 Syapse
> 시스템 및 데이터 통합
> 서울대학교병원의 정밀의료 플랫폼
 결론
Contents

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서울대학교병원 - SNUH

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서울대학교병원 - SNUH

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서울대학교병원 – 전체 규모

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서울대학교병원
서울대학교병원은 세계 최고수준의 교육, 연구, 진료를
통하여 인류가 건강하고 행복한 삶을 누릴 수 있도록 한다.
교육
진료연구

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SNUH (Main Hospital)

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‘안젤리나 졸리 효과’
“나는 내 이야기를 사적으로만 간직하지
않기로 결정했다. 왜냐하면 자신이 암의
위험에 노출되어 있다는 것을 모르고
살아가는 여성들이 많기 때문이다. 삶에는
많은 도전이 동반된다. 우리가 두려워하지
말아야 할 것은, 우리가 통제하고 조율할 수
있는 도전들이 있다는 점이다.”

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Precision Medicine Initiative

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Precision Medicine
 Traditional Approach: Some Benefit, Some Don’t

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Precision Medicine
 Each Patient Receives the Right Medicine

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How Precision Medicine Supports Patients
Cholerton, Brenna et al. Precison Medicine.
The American Journal of Pathology , 2016

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How Precision Medicine Supports Patients

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Genomic Profiling Test Workflow
암 환자의 샘플로부터 질병 別 유전자 패널을 실험표준수행절차에 따라 데이터 생산하고,
분석S/W를 이용하여 변이를 검출한 후, 이에 맞는 약물 정보를 Report를 통해 제공함

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SW + ICT infrastructure

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국내 NGS 기반 유전자 검사/진단 시장
‘17年 Next Generation Sequencing 기반 유전자 검사 신설
- NGS 활용 촉진을 위한 제도 개선 발표 (‘15. 11. 6)
: 공인실험실 검사 (LDT) 제도 도입
- 식약처 'NGS 임상검사실 인증제' 시행 (‘16.08)
- 보건복지부 고시 제2016-271호 (’16.12.29) 「건강보험 행위 급여․비급여 목록표 및 급여 상대가치점수」 일부개정
: NGS 유전자패널검사 신설
- 보건복지부 고시 제2017-15호 (’17.01.31) 「요양급여의 적용기준 및 방법에 관한 세부사항」 일부개정
: NGS 유전자패널검사 급여기준 신설 및 2017.03.01 시행일 고시
: 비유전성 유전자 (고형암, 혈액암), 유전성 유전자 대상 상병 및 필수 유전자 고시
- 보건복지부 고시 제2017-25호 (’17.02.09) 「요양급여비용의 100분의 100 미만의 범위에서 본인부담률을
달리 적용하는 항목 및 부담률의 결정 등에 관한 기준」 일부개정
: NGS 유전자패널검사 선별급여 항목 추가 및 실시조건 신설
: 본인 부담률 및 요양기관 실시조건

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패널 승인 (2018)

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필수 유전자

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연구중심병원

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서울대학교병원 연구중심병원

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연구중심병원 암유닛

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Global Site Ranking in Oncology

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SNUH 정밀의료센터 개소(17.11.14.)

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SNUH – NGS pipeline

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서울대학교병원 NGS 패널

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Clinical NGS Panel Testing Status

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PDF report

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Case : Male/55, Multiple Myeloma

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• Founded in 2012
• Based in San Francisco
• 295 Hospitals, 1100
Oncology providers
• 159,000 new cancer
cases per year
Slide courtesy of Syapse
Syapse

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 Scalable clinical practice of precision oncology
Slide courtesy of Syapse
Syapse

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Clinical Data
Lab Data
MDx Results
VCF
Integrated
Patient view
MDx Report
Molecular
Tumor Board
Clinical
Analytics
Syapse

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SNUH – Syapse: 암병원 심포지엄 (2016.03.25)

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SNUH – Syapse: 정밀의료 심포지엄 (2016.11.11)

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SNUH – Syapse: Timeline

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Syapse: San Francisco HQ (2017.09.21)

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Swedish Cancer Institute, Seattle, WA (2017.09.22)

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…
SNUH – Syapse: Timeline

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SNUH – Syapse: Implementation timeline

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4/9 4/10 4/11 4/12
Monday Tuesday Wednesday Thursday Friday
7:00 AM
Syapse Steering Committee
meeting
8:00 AM
8AM: Hospital Executives
Meeting
8AM Training Methodology
and Plan
9:00 AM
9-12 PM: Tour of SNUH and
introduction to SNUH
workflow
9-10AM Support
Introduction and Planning
9AM-10AM Clinical Trials
Discussion
9AM-10AM : Meeting with
Hospital CEO
10:00 AM
10AM-NOON: CDI Working
Session - Part 2
10AM-NOON SSO and EHR
Embed
10AM-Noon: Lab
Certification Working
Session - Part 311:00 AM
12:00 PM LUNCH LUNCH LUNCH LUNCH
1:00 PM 1PM - 3PM: CDI Working
Session - Part 1
1PM-3PM: Lab Certification
Working Session - Part 1
SNUH - Molecular Tumor
Board
1PM - 3PM: Lab
Certification Working
Session - Part 42:00 PM
2PM - 5PM: Lab
Certification Working
Session - Part 2
3:00 PM
3-5PM: SNUH Testing Plan
3PM-4PM: Molecular Tumor
Board Overview and
Discussion
3PM-4PM: CDI Working
Session - Part 3
4:00 PM
4-PM-5PM: Data Analysis
Discussion OPEN
5:00 PM
5PM - 6PM: Syapse
committee members
meeting: Syapse Product
Roadmap Overview
SNUH– Syapse: On-site visit (2018.04.09 ~ 04.12)

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 TFT 회의 12회: 2018/01/12 ~ 2018/07/06
SNUH – Syapse TFT (2018.01.26)

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 Syapse 운영위원회 회의 8회: 2018/7/13 ~ 현
SNUH – Syapse 운영위원회 (2018.07.13)

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 그랜드 오픈 및 심포지엄 개최
 2018년 7월 13일
SNUH – Syapse: 심포지엄

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HIS in SNUH: Bestcare 2.0

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• Patient
• Diagnosis
• Coverage Account
• Encounter
• Clinical Test Result
• Procedure Order
• Cancer Assessment
• Test Order
• Pathology Report
• Performance Status
• Imaging Report
• Surgery Report
• Radiation Report
• Medication Order
• Medication Administration
• Treatment Plan
Clinical Data Manifest

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Clinical Data
Integration
251
항목
Patient, Diagnosis, Coverage Account,
Encounter, Clinical Test Result,
Procedure Order, Cancer Assessment,
Test Order, Pathology Report,
Performance Status, Imaging Report,
Surgery Report, Radiation Report,
Medication Order, Medication
Administration, Treatment Plan
• 항목 추출:
Data extraction,
Text parsing,
Pattern analysis,
Code mapping
• 항목 추출 결과 검토
• 실제 환자 Data 활용
simulation
• Feedback/수정
• Syapse clinical data
field 검토 
항목 편집/추가
• HIS 추출 항목 선별 및
조건 정의
SNUH – Syapse: Clinical data integration

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SNUH Clinical Data Warehouse

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SUPREME Cohort Mart Platform

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SNUH CDW Syapse: Clinical Interface Overview

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Molecular Data Interface

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{
"testName": "Cancer Profiling Test",
"testMethodology": "NGS Q3",
"testResults": {
"genomicAlteration": [{
"biomarkerName": "BRAF Sensitizing Mutation",
"biomarkerState": "Present",
"wildtypeBiomarker": false,
"hgvsGenomicChange": "NC_000007.13:g.140453136A>T",
"hgvsCodingChange": "NM_004333.4:c.1799T>A",
"hgvsProteinChange": "NP_004324.2:p.Val600Glu",
"gene": "BRAF",
"chromosome": "7",
"cytogeneticLocation": "7q34",
"strandOrientation": "1",
"genomeBuild": "GRCh37/hg19",
"sequenceRegion": {
"exon": "5 of 5"
},
"molecularConsequence": …
}
 FIRST Pan-cancer panel V3.1
 FIRST Glioma panel V1
 FIRST Lung cancer panel V2
 Hemic treatment panel
 Syapse molecular report
JSON format
SNUH – Syapse: Molecular data integration (NGS)

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SNUH – Syapse: Dataflow

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1. 의료분야 데이터의 클라우드 사용 관련 법규
 의료법 시행규칙(제16조) 개정으로 인해, 의료정보에 대한 클라우드 사용이 허용됨
 클라우드(외부보관) 사용 시, 아래의 사항들을 추가적으로 만족하여함을 고지함
구분 추가 내용
의료법
시행규칙
제 16조
제 1항
백업저장장비
네트워크 및 전자의무기록
시스템 보안
물리적 접근 방지 시설과 장비
외부보관시, 필요 시설 추가 장비
아마존웹서비스는
해당 조건을 충족하고,
2018년 해외사업자 최초로
ISMS (정보보호관리체계)
인증을 취득함
SNUH – Syapse: 클라우드 보안

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2. 아마존웹서비스 보안 정책 (인프라 관련) 3. Syapse 보안 정책(솔루션 관련)
외국 서버가 아닌 국내 아마존웹서비스
서버(서울 리전)에 우리병원 관련
Syapse 솔루션 및 데이터가 적재되어 있음
①
2018년 해외 클라우드 사업자 최초로
ISMS(정보보호관리체계) 인증을
취득하여, 국내법을 준수하고 있음
②
※ 단, Syapse 데이터 쉐어링 네트워크에 참여할 경우
익명화하여 해외 AWS 서버 데이터가 저장됨
아마존웹서비스에서는 고객의 서버 및
데이터 베이스에 대한 접근이 불가능하며,
의료기관뿐만 아니라, 정부, 금융 그리고
많은 온라인 서비스들이 클라우드에
데이터를 저장하고 있음
③
국내법 개인정보처리업무 특수조건에 적용되고
있고, 관련 규정을 준수해줄 것을 확약함
[관련 서류를 Syapse 국내 변호사를 통해서 영문화 및 공증을 위한
작업중에 있음]
①
모든 접근 기록이 저장되고 있고, 고객의
데이터는 분리된 데이터존에 안전하게
저장되어 있음
[국내 서울 리전에 저장되고, AWS Virtual Private Cloud 기술을
이용하여 단일 고객 전용 하드웨어에서 실행되어 보안이 높음]
②
사이앱스의 고객 데이터는 암호화하여 저정하고,
전달하고 있다
- 데이터 볼륨 레벨 : LUKS, with aes-xts-plain 그리고 512-bit keys
- 웹 서비스 트래픽 : HTTPS
③
SNUH – Syapse : 클라우드 보안

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 SumoLogic 등 로그 및 데이터를
캡처, 감사 및 분석하는 보안 툴
국내 AWS, 서울대학교병원
해외 AWS, 해외 의료기관
서울대학교병원
CDW
(Clinical Data
Warehouse)
암호화통신인
HTTPS방식 적용
해외 AWS, Syapse Network
SSH(secure shell)이
적용되어,
로그인 할때 해킹을
막을 수 있음
암호화, 익명화
데이터 전송
※ 보안을 위한 적용 기술
 FlowLogs / SOC 모니터링 등
클라우드 서버 접속 기록 및
시도에 대한 로그를 기록함
 TVM, IDS 등 외부 실시간 공격에
대한 대응
: 클라우드 내 우리병원 영역
: Syapse 공유 네트워크 영역
SNUH – Syapse: 데이터 흐름과 적용된 보완 기술

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SNUH Precision Medicine

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임상 다학제 보드

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Case : Male/55, Multiple Myeloma

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FIRST-Heme panel

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HIMSS19

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HIMSS19

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정밀의료 빅데이터 플랫폼 가치

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Conclusions
 SNUH is Korea’s leading hospital utilizing a powerful HIS with CDW and
NGS pipelines.
 Korea Research Driven Hospital Project plays an essential role in
advancing oncology precision medicine platforms in Korea.
 For clinical data, we chose to keep an internal database using a clinical
data warehouse to maintain flexibility.
 Our current platform allows us to connect and share data with other
institutions and create new value.
 We could recommend our experiences and lessons to other institutions
hoping to adopt an international clinico-genomic data handling platform.

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